国家自然科学基金(90920004, 60873150, 60970056); 江苏省自然科学基金(BK2008160)
研究了中文名词性谓词的语义角色标注(semantic role labeling,简称SRL).在使用传统动词性谓词SRL 相关特征的基础上,进一步提出了名词性谓词SRL 相关的特征集.此外,探索了中文动词性谓词SRL 对中文名词性谓词SRL 的影响,并且联合谓词自动识别实现了全自动的中文名词性谓词SRL.在中文NomBank 上的实验结果表明,中文动词性谓词的SRL 合理使用能够大幅度提高中文名词性谓词的SRL 性能;基于正确句法树和正确谓词识别,中文名词性谓词的SRL 性能F1 值达到了72.67,大大优于目前国内外的同类系统;基于自动句法树和自动谓词识别,性能F1 值为55.14.
This paper explores semantic role labeling (SRL) in the Chinese language for nominal predicates. In addition to the widely adopted features of verbal SRL, various nominal predicate-specific features are also explored. Moreover, the nominal SRL performance has been improved by properly integrating features that were derived from a state-of-the-art verbal SRL system. Finally, the paper explains in detail the nominal predicate recognition, which is essential in a fully automatic nominal SRL system. Evaluations on Chinese NomBank show that proper integration of a verbal SRL system significantly improves the performance of a nominal SRL. It also shows that this nominal SRL system achieves the performance of 72.67 in F1-measure on golden parse trees and golden predicates, and outperforms the state-of-the-art nominal SRL systems in the Chinese language; however, the performance drops to 55.14 in F1-measure on automatic parse trees and automatic predicates.